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AI/ML Architect

Laiba Technologies LLCSan Jose, CA🇺🇸United StatesPosted 8 Jul 2026

Why This Role Stands Out

As an AI/ML Architect at Laiba Technologies LLC, you'll drive innovation by building and deploying scalable machine learning systems, gaining valuable experience with cutting-edge MLOps tools. This hybrid role is perfect for a mid-senior professional passionate about end-to-end ML pipeline management and seeking to grow their expertise in a dynamic tech environment. Apply now to shape the future of AI at Laiba Technologies!

Quick Overview

Work Type
Hybrid
Level
Mid Senior

Job Description

We are looking for a skilled MLOps Architect to join our team and help us build, deploy, and maintain robust and scalable machine learning systems. You will be responsible for the full lifecycle of our ML pipelines, from data ingestion to model serving. This is a hands-on role where you will design and implement automated workflows, ensure data quality, and manage model deployments in a production environment.
Responsibilities
  • Data and Feature Pipelines: Design, build, and manage automated data ingestion, transformation, and validation pipelines using services like Kubeflow Pipelines and Vertex AI Pipelines.
  • Feature Engineering: Implement and containerize feature engineering logic for diverse datasets, ensuring reusability and scalability.
  • Data Validation: Integrate and manage data validation processes, including leveraging advanced techniques like AI Agents and the Generative Language API to automatically detect and remediate data quality issues.
  • Model Training and Experimentation:
  • Set up and maintain automated continuous training (CT) pipelines using Vertex AI Pipelines (Schedules) and Cloud Scheduler.
  • Implement experiment tracking to log and compare model parameters, metrics, and artifacts.
  • Configure and execute Hyperparameter Tuning jobs using Vertex AI Training to optimize model performance.
  • Model Management: Establish a robust Model Versioning system to manage and store model artifacts securely in a centralized repository (Cloud Storage).
  • Deployment and Serving:
  • Containerize ML models and their dependencies using Docker and manage images with Artifact Registry.
  • Build and maintain CI/CD workflows for ML models, ensuring seamless and automated deployment.
  • Configure and manage low-latency production serving environments using Vertex AI Endpoints for real-time inference.
Qualifications
  • Strong experience with Google Cloud Platform (Google Cloud Platform) services, specifically in the MLOps and ML domain (Vertex AI, Kubeflow, Cloud Storage, Artifact Registry).
  • Proven ability to design and implement end-to-end ML pipelines for data management, model training, and deployment.
  • Hands-on experience with containerization technologies like Docker.
  • Familiarity with CI/CD practices and pipeline automation.
  • Knowledge of ML frameworks like TensorFlow, and experience with experiment tracking and hyperparameter tuning.
  • Excellent problem-solving skills and a strong understanding of the ML lifecycle.
Experience with the Generative Language API (Gemini model) or other AI Agent integrations is a plus.

Skills

Docker
MLOps
Machine Learning
Google Cloud
TensorFlow

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